TransLoc: A Heterogeneous Knowledge Transfer Framework for Fingerprint-Based Indoor Localization

Abstract

Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to real time changes in the environmental dynamics of the target space due to three remarkable shortcomings: a) redundant knowledge in source domain may lead to “negative transfer”; b) the required target domain samples to calculate the distributions are unrealistically feasible for real-time positioning; c) they cannot transfer knowledge efficiently across domains with heterogeneous feature spaces. In this paper, we propose TransLoc, a heterogeneous knowledge transfer framework for fingerprint-based indoor localization, which can perform knowledge transfer efficiently even with only one sample in the target domain. Specifically, we first refine the source domain according to the target domain by removing redundant knowledge in the source domain. Then, we derive a cross-domain mapping, which transfers the specific knowledge of one domain to another domain, to construct a homogeneous feature space. In this new feature space, the transfer weights are computed for training a classifier for target location prediction. To further train the framework efficiently, we combine the mapping and weights learning into a joint objective function and solve it by a three-step iterative optimization algorithm. Extensive simulation and real-world experimental results verify that TransLoc not only significantly outperforms state-of-the-art methods but is also very robust to changing environment.